Politicians.py

import streamlit as st #deployment
from langchain.chains.llm import LLMChain #individual chain
from langchain.chains.sequential import SequentialChain #combine chains
from langchain_groq import ChatGroq # llm model
from langchain.prompts import PromptTemplate #prompt for query

st.title('LEADER DATABASE') #title
leader_name=st.text_input('Enter The Famous leader_name from India.')

sub=st.button('SUBMIT')

LLM_model=ChatGroq(temperature=0.6,
                   groq_api_key='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')

prompt1=PromptTemplate(input=['A'],
                       template='tell me about {A} in 20 words.') 

chain1=LLMChain(llm=LLM_model,prompt=prompt1,output_key='person')

prompt2=PromptTemplate(input=['person'],
                       template='what is date of birth of this {person}.')

chain2=LLMChain(llm=LLM_model,prompt=prompt2,output_key='dob')  

prompt3=PromptTemplate(input=['dob'],
                       template='mention how many time win elections {dob}.') 

chain3=LLMChain(llm=LLM_model,prompt=prompt3,output_key='election win')


#combine all the chains in sequence
parent=SequentialChain(chains=[chain1,chain2,chain3],
                       input_variables=['A'],
                       output_variables=['person','dob','election win']
                       )

if sub:
    st.write(parent({'A':leader_name}))